Hi Elaine
I do not use it very often. I programmed it to mimic Minitab functions
(partly) with some adons from czech statistics textbook written by
M.Meloun (meloun militky statistics - first hit in google)
Basically you can have your data in some data frame or they can be as
separated vectors. The function itself expects input of 3 vectors, but you
can easily to modify it for imput as three column data frame.
Here are some explanations of terms used. I post it also to Rlist for
future reference.
operator is a person performing the task (lab worker, ...) - expected a
factor, but is ok if numeric or character vector
vzorek is sample analysed - expected a factor, but is ok if numeric or
character vector
vysledek is a result of analysis - expected numeric vector
sigma is some coefficient
DM is lower limit, HM is upper limit, toler is range either computed from
DM and HM or set by you - addon from mentioned textbook
plotit is switch for enabling or disabling plot function - expected
logical value
dig is number of digits for rounding result
The function is based on anova
Here is an example
test<-expand.grid(operator=letters[1:4],vzorek=LETTERS[5:8])
test<-sapply(test,rep,4)
test<-as.data.frame(test)
set.seed(1)
test$result<-runif(64)
test$result
[1] 0.26550866 0.37212390 0.57285336 0.90820779 0.20168193 0.89838968
[7] 0.94467527 0.66079779 0.62911404 0.06178627 0.20597457 0.17655675
...
ORanal(test$lab.work, test$sample, test$result, dig=4)
$tabulka
rozptyl smer.odch 5.15 sm.odch proc.rozpt. proc.sm.odch
Vzorky 0.0033 0.0573 0.2949 4.5105 21.2378
Oper 0.0005 0.0221 0.1137 0.6703 8.1872
Interakce 0.0031 0.0560 0.2886 4.3194 20.7832
Reprod 0.0027 0.0515 0.2653 3.6491 19.1026
Opak 0.0721 0.2685 1.3826 99.1386 99.5684
O&R 0.0694 0.2635 1.3569 95.4895 97.7188
Suma 0.0727 0.2696 1.3886 100.0000 100.0000
$vartab
Df Sum Sq Mean Sq F value Pr(>F)
vzorek 3 0.3359 0.111980 1.5537 0.2128
operator 3 0.2019 0.067311 0.9339 0.4316
vzorek:operator 9 0.5356 0.059514 0.8257 0.5957
Residuals 48 3.4596 0.072075
$kategorie
[1] 1
$rozlisitelnost
[1] 0.516411
Here is:
in $tabulka table like in Minitab output
rozptyl = variance
smer.odch = standard deviation
5.15 smer.odch = standard deviation * 5.15
proc.rozpt = variance percentage
proc.sm.odch = standard deviation percentage
Vzorky = Samples
Oper = Operators
Interakce = Interaction
Reprod = Reproducibilty
Opak = Repeatability
O&R = sum of Reproducibility and Repeatability
Suma = Total
in $vartab
anova table (output from aov call)
in $kategorie
no of categories which can be estimated safely with method used for
analysis
in $rozlisitelnost
some value from Meloun/Militky book
Basically the function takes input of 3 vectors of equal length(operator,
sample, result) makes analysis of variance and takes third column from
anova table
fit<-aov(result~lab.work*sample, data=test)
anova(fit)
Analysis of Variance Table
Response: result
Df Sum Sq Mean Sq F value Pr(>F)
lab.work 3 0.2019 0.067311 0.9339 0.4316
sample 3 0.3359 0.111980 1.5537 0.2128
lab.work:sample 9 0.5356 0.059514 0.8257 0.5957
Residuals 48 3.4596 0.072075
anova(fit)[,3]
[1] 0.06731072 0.11197975 0.05951360 0.07207455
and do all required computation which is than put into result.
Do not ask me why it is computed like that I made it about 10 years ago.
I would need to go to textbook(s), find appropriate chapter and study it
to be able to say why it is computed like that. If you have some older
result from any textbook or package together with data, you can easily
check if my function gives the same results.
Feel free to modify it for your purpose. Especially plotting is not very
sophisticated so you can modify it to give you proper labeling. Regarding
not many posts about GR&R analysis you need to ask in R help. Probably R
is most often used for research or finance and for those areas such type
of specialised analysis is not of much use. You need to have structured
data, like in above example, which is quite often not the case in those
areas.
If you needed some more explanation feel free to ask me.
Regards
Petr
----------------------------------------------------------
ORanal<-function(operator,vzorek,vysledek, sigma=5.15, DM=NULL ,HM=NULL,
toler=NULL, plotit=F, dig=4)
{
if (!is.factor(vzorek)) vzorek<-factor(vzorek)
if (!is.factor(operator)) operator<-factor(operator)
if (is.null(toler)) toler<-abs(HM-DM)
fit<-aov(vysledek~vzorek*operator)
volop<-nlevels(operator[,drop=T])
volvzor<-nlevels(vzorek[,drop=T])
opak<-length(operator)/(volop*volvzor)
ctver<-anova(fit)[,3]
result=NULL
result[3]<-(ctver[3]-ctver[4])/opak
result[5]<-ctver[4]
result[1]<-(ctver[1]-ctver[4]-result[3]*opak)/(volop*opak)
result[2]<-(ctver[2]-ctver[4]-result[3]*opak)/(volvzor*opak)
if (result[2]<0) result[2]<-0
result[4]<-result[2]+result[3]
result[6]<-result[4]+result[5]
result[7]<-result[1]+result[6]
result<-abs(result)
proc<-result/result[7]*100
smodch<-sqrt(result)
smodch5<-smodch*sigma
proc.smodch<-smodch/smodch[7]*100
proc.toler<-smodch5/toler*100
result<-cbind(result,smodch,smodch5,proc,proc.smodch,proc.toler)
result<-data.frame(result,row.names=c("Vzorky","Oper","Interakce","Reprod","Opak","O&R","Suma"))
meno<-c("rozptyl","smer.odch", "5.15 sm.odch" ,"proc.rozpt."
,"proc.sm.odch", "toler.sm.odch")
velik<-dim(result)[2]
names(result)<-meno[1:velik]
suma<-summary(fit)
kateg<-trunc(smodch[1]/smodch[6]*1.41)+1
if (plotit)
{
par(mfrow=c(3,2))
posice<-volvzor*1:volop
mat<-aggregate(vysledek,list(vzorek,operator),mean,na.rm=T)
mat1<-aggregate(vysledek,list(vzorek,operator),sd,na.rm=T)
#fig1
hmdm<-c(mean(mat$x)+(mean(mat1$x)/.9)/sqrt(opak)*3,
mean(mat$x)-(mean(mat1$x)/.9)/sqrt(opak)*3)
plot(mat$x,type="b",ylab="Prumery")
abline(h=mean(mat$x),col=3)
abline(h=hmdm,col=2)
abline(v=posice+.5,col="grey",lwd=2,lty=2)
text(posice-volvzor/2,max(mat$x),levels(operator[,drop=T]))
#fig2
# mat<-reshape(mat,direction="wide",idvar="Group.1",timevar="Group.2")
#
matplot(as.matrix(mat[,2:(1+volop)]),type="b",pch=1:volop,col=1:volop,ylab="Prumer",axes=F)
# axis(side=1,at=1:volvzor,labels=levels(vzorek[,drop=T]))
# box()
# axis(2)
#
legend(1,min(mat[,2:(1+volop)]),levels(operator[,drop=T]),pch=1:volop,col=1:volop,cex=.8,horiz=T,yjust=0,xjust=0)
mat<-reshape(mat,direction="wide",idvar="Group.1",timevar="Group.2")
matplot(as.matrix(mat[,2:(1+volop)]),type="b",pch=1:volop,col=1:volop,ylab="Prumer",axes=F)
axis(side=1,at=1:volvzor,labels=levels(vzorek[,drop=T]))
box()
axis(2)
legend(1,min(mat[,2:(1+volop)]),levels(operator[,drop=T]),pch=1:volop,col=1:volop,cex=.8,horiz=T,yjust=0,xjust=0)
#fig3
hmdm<-c(mean(mat1$x)*sqrt(qchisq(.99865,opak,lower.tail=F)/(opak-1)),
mean(mat1$x)*sqrt(qchisq(.99865,opak)/(opak-1)))
plot(mat1$x,type="b",ylab="Smerod. odch")
abline(h=mean(mat1$x),col=3)
abline(h=hmdm,col=2)
abline(v=posice+.5,col="grey",lwd=2,lty=2)
text(posice-volvzor/2,max(mat1$x),levels(operator[,drop=T]))
#fig4
boxplot(split(vysledek,operator),notch=T)
#fig5
barplot(t(as.matrix(result[c(1,4,5,6),4:velik])),beside=T)
#fig6
boxplot(split(vysledek,vzorek),notch=T)
par(mfrow=c(1,1))
mtext(paste("R+R analýza", deparse(substitute(vysledek))), side=3,
line=2, cex=1.5)
}
rozlis<-smodch[6]*qnorm(0.975)
list(tabulka=round(result,dig),vartab=suma,kategorie=kateg,rozlisitelnost=rozlis)
}
#--------------------------------------------------------------------------------------------------------
Elaine Jones <jon...@us.ibm.com> napsal dne 05.08.2011 21:41:10:
>
> Hi Petr,
>
> I found this post on the R-help website:
> http://www.mail-archive.com/r-help@r-project.org/msg00447.html
>
> A coworker just asked me to help him with some GR&R studies. Could you
> tell me about how your function works? How is the input data
structured?
> What is vysledek? (the response variable?)
>
> I was surprised to see very few posts about GR&R on the R-help website,
and
> packages. Any help you can offer is appreciated.
>
> [R] Odp: GR&R - Best methods in R
>
>
> Petr PIKAL
> Mon, 17 Sep 2007 01:04:27 -0700
>
>
> Hi
>
> below you can find a function I wrote to mimic a Minitab R+R
> procedure. It
> is intended for myself only so it is not commented. If you want to
> know
> how it functions contact me off-list.
>
> Regards
>
> Petr
>
>
>
> Sincerely,
> **************** Elaine McGovern Jones ************************
>
> Phone: 408 705-9588 Internal tieline: 587-9588
> jon...@us.ibm.com
>
>
>
>
>
>
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